Near-Real-Time DMSP SSMIS Daily Polar Gridded Sea Ice Concentrations, Version 2
Data set id:
This is the most recent version of these data.
Version update reflects the conversion of the data set from binary to netCDF.
This data set provides a Near-Real-Time (NRT) map of sea ice concentrations for both the Northern and Southern Hemispheres.
SEA ICE CONCENTRATION
DMSP 5D-3/F16, DMSP 5D-3/F17, DMSP 5D-3/F18
1 November 2021 to present
Spatial Reference System(s):
NSIDC Sea Ice Polar Stereographic North
NSIDC Sea Ice Polar Stereographic South
Blue outlined yellow areas on the map below indicate the spatial coverage for this data set.
Strengths and Limitations
- Useful for large-scale monitoring of sea ice
- Microwave observations provide surface snow and ice coverage during cloudy and night-time (including polar night) conditions (Cavalieri et al., 1999)
- Thorough inter-calibration between sensors for consistency throughout the record (Cavalieri et al., 1999; Cavalieri et al., 2012)
- Less sensitive to temperature variations because it uses ratios instead of differences (Comiso et al., 1997)
- Concentrations are generally reliable within the ice pack (away from the ice edge) during cold (non-melt) conditions (Comiso et al., 1997)
- Useful input/validation of climate model simulations (National Center for Atmospheric Research Staff, 2017)
- Low spatial resolution (25 km gridded) limits detail on concentration and precision of sea ice edge; is unsuitable for operational/navigational support (Cavalieri et al., 1999)
- Underestimates sea ice concentration during melt season (Kern et al., 2020) and/or when the ice is thin (Ivanova et al., 2015)
- Higher uncertainties in Antarctica due to flooded snow and other ice characteristics (Comiso et al., 1997)
- Algorithm coefficients are fixed for a given sensor, so biases can occur if characteristic surface conditions change (Cavalieri et al., 1999)
- False coastal ice can occur due to mixed land and ocean within a sensor footprint (Cavalieri et al., 1999)
- Near-real-time product with no planned reprocessing for long-term consistency; should not be used to derive long-term trends in sea ice or snow (Cavalieri et al., 1999)
Data Access & Tools
General Questions & FAQs
NSIDC currently archives passive microwave sea ice concentration products based on two algorithms: the NASA Team algorithm and the Bootstrap algorithm. Both algorithms were developed by researchers at the NASA Goddard Space Flight Center in the 1980s.
How to Articles
Many NSIDC DAAC data sets can be accessed using the NSIDC DAAC's Data Access Tool. This tool provides the ability to search and filter data with spatial and temporal constraints using a map-based interface. Users have the option to
How do I convert NSIDC SMMR-SSM/I-SSMIS data products in polar stereographic projections from NetCDF to binary?
The NSIDC provides Python scripts for reformatting specific NSIDC SMMR-SSM/I-SSMIS data products in polar stereographic projections from NetCDF to binary.